How to Extract Column Labels for True Values in Pandas Rows – Simple Pythonic Solutions

Introduction

In a Python community chat, a user asked how to process a Pandas DataFrame where each row contains boolean attributes and the goal is to return a list of column labels whose value is 1 for that row. For example, for the row labeled AUS the desired output is [DEV_f1, URB_f0, LIT_f1, IND_f1, STB_f0].

Solution 1 – Iterative Method

The first responder suggested iterating over the DataFrame rows and collecting the column names where the value equals 1. This approach was illustrated with a screenshot of the code implementation.

Solution 2 – Optimized Approach

A second contributor offered a more concise solution, also shown in an image, that leverages Pandas vectorized operations to achieve the same result with fewer lines of code.

Solution 3 – Using apply (Most Pythonic)

The final recommendation highlighted the use of the apply function combined with a lambda expression to extract the desired column labels in a clean, Pythonic way. This method was praised as the most readable and efficient among the options.

Conclusion

The article summarizes the three approaches, emphasizing that while many methods exist, the apply -based solution is generally preferred for its clarity. It also provides practical advice for asking technical questions: share a small, anonymized sample of the data, include reproducible code snippets, and attach error screenshots when relevant.